Updates

Model and report changes

  1. Over January 2022 our model has been redesigned to incorporate the booster vaccination campaign and to be able to capture, in an unbiased way, the ecological effects of the emergence and subsequent dominance of the Omicron variant. In particular, we have stratified the entire model by vaccination status so that we can more accurately capture the impacts of waning immunity. The duration of immunity following infection is assumed to have a mean of around 18 months, but this falls rapidly around the time of the emergence of omicron to account for the immune escape of this variant. The waning of vaccine-derived immunity is captured through an assumed drop in vaccine efficacy at this time.
  2. A further modification to the model has been required due to the scaling back of the testing programme. Since March 2020 we have been calibrating the model against reported deaths within sixty days of a positive test. We now report the results from a model that uses data on new diagnoses in hospitals (where testing is still routine). The structure of the model remains similar but we make new assumptions about the proportion of infections that are detected by hospital-based testing, and the time taken post-infection for this detection to occur.
  3. We have extended the use of serological data to use samples taken beyond the first wave of the pandemic. The samples are those collected by NHS Blood and Transplant using the Roche-N assay, which measures the prevalence of infection-acquired antibodies in the population.
  4. The model also accounts for a different susceptibility to infection in each adult age group (no prior information is used); and for the under-15s, (using prior information from Viner et al, 2020, which estimates children to be less likely to acquire infection when in contact with an infectious individual).
  5. The model has the ability to incorporate estimates of community prevalence, by region and age group, from the Office of National Statistics COVID-19 Infection /Survey (ONS CIS; see Data Sources for details). These are included weekly since the outset of the Survey in May 2020 for the age groups >4 years to provide a more timely source of information on recent trends in incidence.
  6. The underlying probability of an unvaccinated individual testing positive in hospital following infection with SARS-CoV2 (the apparent infection-hospitalisation rate, IHR) is allowed to change gradually over the course of 30 days every (approximately) 100 days. This is designed to reflect fluctuations due to seasonal effects, demand on healthcare services or the emergence of new virus variants of differing severity.
  7. The “Epidemic summary” only reports the current value for the IHR by age. To visualise how this has changed over time in our model, see the IHR tab in the ‘Infections and Deaths’ section of the report. The quantity that is now plotted under this tab is the probability of being diagnosed in hospital if infected, taking into account the impact of the immunisation programme - it is an average of a lower rate of hospitalisation among vaccinated individuals and a higher rate among the unvaccinated.
  8. The attack rate table shows the proportion of individuals to have ever been infected over the course of the pandemic. Under any “Infections” tab, what is presented is the totality of all infections, including reinfections, and will therefore show values higher than the attack rate might suggest.

Updated findings

  1. The estimated number of new daily infections on 29th July across England has dropped 75% from our last report and is now 75,200 (62,300–89,900, 95% credible interval) infections per day, corresponding to a national daily infection rate of 134 per 100k population. The highest rate remains in the North, with the highest rate that of the North West (NW) with 245 infections per 100K population, slightly ahead of Yorkshire and The Humber (YH) with 210 and the North East (NE) on 169. All other regions are below the national average, with the East Midlands (EM) and West Midlands (WM) on 117 and 112 respectively, both the South West (SW) and East of England (EE) on 108. Greater London (GL) and the South East (SE) are estimated to have the lowest rates of infection with both below 100 infections per 100k per day. Note that a substantial proportion of these infections will be asymptomatic.
  2. The model predictions for the daily number of new diagnoses in hospitals reached a low of 1.8k per day around July 9th and has fallen sharply since, currently halving in number every 18.0 days across the country. We forecast that by August 19th, there will be 350–600 new diagnoses in hospitals per day.
  3. The infection growth rate for England is -0.04 (-0.06– -0.03) per day. This means that, nationally, the number of infections is decreasing, corresponding to an Rt of around 0.65–0.70.
  4. All regions are estimated to have Rt less than 1. The decline is fastest in the SE and slowest in YH.
  5. Our estimates for the attack rate, the proportion of a population who have ever been infected, is up to 86.7% nationwide, and is now over 90% in three regions, NE, WM and GL. The estimated total number of infections to date (69.2m) far exceeds the size of the population of England, due to the presence of reinfections.
  6. Note that the hospitalised diagnosis data used are only partially informative on Rt over the last two weeks. Therefore, the estimate for current incidence, Rt and the forecast of daily numbers of deaths are likely to be subject to significant revision.

Interpretation

Across England, we estimate that since July 9th the daily number of diagnoses in hospitals is declining. PCR-positive infection prevalence is estimated to be similarly decreasing in all ONS regions and in all age groups.

Overall, since December 1st 2021, the Omicron variant has resulted in the cumulative number of infections increasing from 24.8 million individuals to 69.2 million. In total, 30% of all infections over the course of the entire pandemic are re-infections and this fraction is only expected to increase as currently we estimate 51% of new infections are first infections.

During the Omicron-era, December 2021-May 2022, we estimate a fall of around 75% in the IHR in the age groups over 45 and a decline of over 50% in the under 45 age groups. Since May, we estimate an increase in the IHR in the under-45s, though not back to pre-Omicron levels. There are small increases in the 45-64 and 65-74 age groups, whilst for the over-75s there is still evidence of an improving IHR. This phenomenon in the under-45s could, in part, be explained by a reduced protection from the vaccines against severe symptoms due to infection with BA.4 and BA.5. Additionally, the model does not yet include the second booster vaccinations that the over-75 age group have received, so any consequent improvement in health outcomes will be attributed to a drop in IHR. The overall IHR increases only modestly over this most recent period, from 0.7% to 0.8%. The over-75s still have the highest IHR at 3.3% (3.2%–3.4%).

Summary

Real-time tracking of an epidemic, as data accumulate over time, is an essential component of a public health response to a new outbreak. A team of statistical modellers at the MRC Biostatistics Unit (BSU), University of Cambridge, are working to provide regular now-casts and forecasts of COVID-19 infections and deaths. This information feeds directly to the SAGE sub-group, Scientific Pandemic Influenza sub-group on Modelling (SPI-M), and to regional Public Health England (PHE) teams.

Methods

We fit a transmission model (Birrell et al. 2020) to a number of data sources (see ‘Data Sources’), to reconstruct the number of new COVID-19 infections over time in different age groups and NHS regions, estimate a measure of ongoing transmission and predict the number of new COVID-19 deaths.

Data sources

We use:

  1. Data on COVID-19 confirmed deaths from the Public Health England (PHE) line-listing This consists of a combination of deaths notified to:
    • the Demographics Batch Service (DBS), a mechanism that allows PHE to submit a file of patient information to the National Health Service spine for tracing against the personal demographics service (PDS). PHE submit a line list of patients diagnosed with COVID-19 to DBS daily. The file is returned with a death flag and date of death updated (started 20th March, 2020).
    • NHS England, who report data from NHS trusts relating to patients who have died after admission to hospital or within emergency department settings.
    • Health Protection Teams (HPTs), resulting from a select survey created by PHE to capture deaths occurring outside of hospital settings, e.g. care homes (started 23rd March, 2020)
  2. Data on antibody prevalence in blood samples from a PHE survey of NHS Blood Transfusion (NHSBT) donors.

Data are stratified into eight age groups: <1, 1-4, 5-14, 15-24, 25-44, 45-64, 65-74, 75+, and the NHS England regions (North East and Yorkshire, North West, Midlands, East of England, London, South East, South West).

  1. Published information on the the natural history of COVID-19 (Verity et al., 2020; Li et al, 2020)
  2. Information on contacts between different age groups from:
    • A Survey that describes relative rates of contacts between different age groups (Mossong et al. 2008).
    • Google Community Mobility reports, informing the changes in people’s mobility over the course of the pandemic, particularly after the March 23rd lockdown measures.
    • The ONS’ time use survey, which in conjunction with the google mobility study, allows estimation of the changing exposure to infection risk over time.
    • Data from the Department for Education describing the proportion of children currently attending school.
  3. Daily data on the numbers of people getting immunised by age-group and region. These data are derived from the National Immunisation Management Service (NIMS). These data includes all COVID-19 immunisations administered at hospital hubs, local immunisation service sites such as GP practices, and dedicated immunisation centres.
  4. Data on new diagnoses in hospital testing. This is a composite dataset derived from two sources
    • February-October 2020: The NHS England Secondary Uses Service (SUS) dataset, a collection of complete, accurate information on all hospitalisations with and due to COVID-19 infection. This is a comprehensive dataset but lacks the timeliness required for real-time surveillance, as individual records are completed for an individual and recorded on the system only upon death or hospital discharge.
    • October 2020 onwards: NHS England and NHS Improvement COVID-19 Hospital Daily Situation Reports. Prior to this date, the NHS Situation Reports did not have the age-group granularity required by the model.

Epidemic summary

Current \(R_t\)

Value of \(R_t\), the average number of secondary infections due to a typical infection today.

Number of infections

Attack rate

The percentage of a given group that has been infected.

By region

By age

Current IHR

Change in infections incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England -0.04 -0.06 -0.03
East of England -0.06 -0.11 -0.03
East Midlands -0.05 -0.09 -0.01
London -0.07 -0.10 -0.03
North East -0.05 -0.09 -0.02
North West -0.03 -0.06 -0.01
South East -0.08 -0.13 -0.04
South West -0.06 -0.09 -0.03
West Midlands -0.07 -0.11 -0.03
Yorkshire and The Humber -0.03 -0.07 0.00

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 15.07 11.74 22.26
East of England 11.18 6.23 25.33
East Midlands 14.62 7.33 73.79
London 9.77 6.32 19.83
North East 14.97 7.64 44.87
North West 20.43 11.28 122.57
South East 8.06 5.03 16.67
South West 11.96 7.56 22.23
West Midlands 9.82 5.87 21.19
Yorkshire and The Humber 23.17 10.21 150.37

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA NA NA
East of England NA NA NA
East Midlands NA NA NA
London NA NA NA
North East NA NA NA
North West NA NA NA
South East NA NA NA
South West NA NA NA
West Midlands NA NA NA
Yorkshire and The Humber NA NA NA

Change in admissions incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England -0.04 -0.04 -0.03
East of England -0.04 -0.06 -0.02
East Midlands -0.04 -0.06 -0.01
London -0.05 -0.07 -0.03
North East -0.04 -0.06 -0.01
North West -0.02 -0.04 0.00
South East -0.06 -0.08 -0.04
South West -0.04 -0.06 -0.02
West Midlands -0.05 -0.07 -0.03
Yorkshire and The Humber -0.02 -0.04 0.00

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 18.03 15.08 22.53
East of England 15.37 10.34 33.39
East Midlands 19.39 11.18 89.33
London 13.41 10.09 22.85
North East 19.19 11.66 59.98
North West 35.99 18.49 NA
South East 11.50 8.79 18.85
South West 15.87 11.33 29.04
West Midlands 13.56 9.80 24.89
Yorkshire and The Humber 34.56 15.49 NA

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA NA NA
East of England NA NA NA
East Midlands NA NA NA
London NA NA NA
North East NA NA NA
North West NA 266.39 NA
South East NA NA NA
South West NA NA NA
West Midlands NA NA NA
Yorkshire and The Humber NA 269.21 NA 
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Infections and deaths

The shaded areas show periods of national lockdown, the green lines the dates (once confirmed) of the steps in the roadmap in the UK Governement’s COVID-19 Response – Spring 2021, and the red line shows the date these results were produced (29 Jul).

Infection incidence

By region

By age

Cumulative infections

By region

By age

Admissions incidence

By region

By age

Cumulative admissions

By region

By age

IHR

Prob \(R_t > 1\)

The figure below shows the probability that \(R_t\) is greater than 1 (ie: the number of infections is growing) in each region over time. Clicking the regions in the legend allows lines to be added or removed from the figure.

\(R_t\)

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